• Kommineni Sathvika
• Varshitha E
• Dileep T
• Anoop B
• Tanushree Mohapatra

Sleep disorders such as insomnia and sleep apnea are growing public health concerns due to their impact on physical and mental well-being. Conventional diagnostic methods, including polysomnography, though accurate, are time-consuming, costly, and require clinical settings. The integration of machine learning (ML) and deep learning (DL) techniques has opened new avenues for automated, scalable, and cost-effective detection of sleep disorders. This paper presents a critical review of recent advancements in sleep disorder classification using ML and DL approaches. The reviewed studies utilize various data sources, including physiological signals and lifestyle attributes, applying methods such as feature extraction, hybrid model design, and ensemble learning. Algorithms like Random Forest, Support Vector Machines , and Convolutional Neural Networks demonstrate notable performance, with some achieving accuracies up to 99%. This review evaluates these models in terms of accuracy, robustness, and clinical relevance, while also addressing challenges such as data imbalance, computational complexity, and model interpretability. The findings aim to guide future research towards developing efficient, accurate, and interpretable systems for the early detection and diagnosis of sleep disorders.

Sleep Disorders, Machine Learning, Deep Learning, EEG, Sleep Staging, Classification Models, Healthcare AI.

"Sleep Sense: Machine Learning Approaches for Accurate Sleep Disorder Diagnosis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.b718-b723, May-2025, Available :http://www.jetir.org/papers/JETIR2505184.pdf

"Sleep Sense: Machine Learning Approaches for Accurate Sleep Disorder Diagnosis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppb718-b723, May-2025, Available at : http://www.jetir.org/papers/JETIR2505184.pdf